Animal pose estimation in two dimensions has been recently made possible using convolutional neural networks that allow keypoint detections throughout a recorded video based on the training data labeled by the user [1]}, [2]}.
If the goal is to obtain the animal pose in three dimensions, it is possible to combine the 2D keypoint detections from multiple synchronously operated cameras, and triangulate the points into a global 3D space [3]}.
However, prior to the triangulation, the cameras need to be calibrated using a target, which is often an erroneous process in practice that can result in skewed projections of points into the global space.
Moreover, during many experiments in behavioral neuroscience, it is difficult to establish continuous 3D tracking of keypoints over time as often it can not be guaranteed that two or more cameras have a view on all tracked body parts.
This can occur due to different reasons; difficulties in mounting the cameras in the desired angles, occlusion of objects or others animals, and self occlusion of the animal itself.
To overcome such shortcomings for 3D pose estimation in humans, lift pose models have been developed, which aim to infer the 3D pose directly from a single camera view [4]}, [5]}, [6]}, [7]}.
